A hybrid approach for fault tolerance in robotics

This paper studies the difference between the human behaviours for fault tolerance with a pseudo inverse reconfiguration approach for fault tolerance of robotic arms. If this difference is well understood then it can be used to introduce a hybrid approach for fault tolerant motion of robotic arms. The proposed approach is expected to combine human fault-tolerance dexterity and advantages of a model based fault tolerance. The main aim is to add human dexterity for fault tolerance of robotic arms.

Multiple actuator fault tolerance for static nonlinear systems based on minimum velocity jump

Static nonlinear systems are common when the model of the kinematics of mechanical or civil structures is analyzed for instance kinematics of robotic manipulators. This paper addresses the maximum effort toward fault tolerance for any number of the locked actuators failures in static nonlinear systems. It optimally reconfigures the inputs via a mapping that maximally accommodates the failures. The mapping maps the failures to an extra action of healthy actuators that results to a minimum jump for the velocity of the output variables. Then from this mapping, the minimum jump of the velocity of the output is calculated. The conditions for a zero velocity jump of the output variables are discussed. This shows that, when the conditions of fault tolerance are maintained, the proposed framework is capable of fault recovery not only at fault instances but also at the whole output trajectory. The proposed mapping is validated by three case studies.

Optimal fault tolerant Jacobian matrix generators for redundant manipulators

The design of locally optimal fault-tolerant manipulators has been previously addressed via adding constraints on the bases of a desired null space to the design constraints of the manipulators. Then by algebraic or numeric solution of the design equations, the optimal Jacobian matrix is obtained. In this study, an optimal fault-tolerant Jacobian matrix generator is introduced from geometric properties instead of the null space properties. The proposed generator provides equally fault-tolerant Jacobian matrices in R3 that are optimally fault tolerant for one or two locked joint failures. It is shown that the proposed optimal Jacobian matrices are directly obtained via regular pyramids. The geometric approach and zonotopes are used as a novel tool for determining relative manipulability in the context of fault-tolerant robotics and for bringing geometric insight into the design of optimal fault-tolerant manipulators.

Optimal fault-tolerant robotic manipulators

This thesis addresses “Optimal Fault-Tolerant Robotic Manipulators” for locked-joint failures and consists of three components. It begins by investigating the regions of workspace where the manipulator can operate with high reliability. It then continues with an efficient deployment of kinematic redundancies for fault-tolerant operation. Finally, it presents a novel method for design of optimal fault-tolerant manipulators.

Well-conditioned configurations of fault-tolerant manipulators

Fault-tolerant motion of redundant manipulators can be obtained by joint velocity reconfiguration. For fault-tolerant manipulators, it is beneficial to determine the configurations that can tolerate the locked-joint failures with a minimum relative joint velocity jump, because the manipulator can rapidly reconfigure itself to tolerate the fault. This paper uses the properties of the condition numbers to introduce those optimal configurations for serial manipulators. The relationship between the manipulator's locked-joint failures and the condition number of the Jacobian matrix is indicated by using a matrix perturbation methodology. Then, it is observed that the condition number provides an upper bound of the required relative joint velocity change for recovering the faults which leads to define the optimal fault-tolerant configuration from the minimization of the condition number. The optimization problem to obtain the minimum condition number is converted to three standard Eigen value optimization problems. A solution is for selected optimization problem is presented. Finally, in order to obtain the optimal fault-tolerant configuration, the proposed method is applied to a 4-DoF planar manipulator.

The hidden permutation model and location-based activity recognition

Permutation modeling is challenging because of the combinatorial nature of the problem. However, such modeling is often required in many real-world applications, including activity recognition where subactivities are often permuted and partially ordered. This paper introduces a novel Hidden Permutation Model (HPM) that can learn the partial ordering constraints in permuted state sequences. The HPM is parameterized as an exponential family distribution and is flexible so that it can encode constraints via different feature functions. A chain-flipping Metropolis-Hastings Markov chain Monte Carlo (MCMC) is employed for inference to overcome the O(n!) complexity. Gradient-based maximum likelihood parameter learning is presented for two cases when the permutation is known and when it is hidden. The HPM is evaluated using both simulated and real data from a location-based activity recognition domain. Experimental results indicate that the HPM performs far better than other baseline models, including the naive Bayes classifier, the HMM classifier, and Kirshner's multinomial permutation model. Our presented HPM is generic and can potentially be utilized in any problem where the modeling of permuted states from noisy data is needed.

Indoor location prediction using multiple wireless received signal strengths

This paper presents a framework for indoor location prediction system using multiple wireless signals available freely in public or office spaces. We first propose an abstract architectural design for the system, outlining its key components and their functionalities. Different from existing works, such as robot indoor localization which requires as precise localization as possible, our work focuses on a higher grain: location prediction. Such a problem has a great implication in context-aware systems such as indoor navigation or smart self-managed mobile devices (e.g., battery management). Central to these systems is an effective method to perform location prediction under different constraints such as dealing with multiple wireless sources, effects of human body heats or mobility of the users. To this end, the second part of this pa- per presents a comparative and comprehensive study on different choices for modeling signals strengths and prediction methods under different condition settings. The results show that with simple, but effective modeling method, almost perfect prediction accuracy can be achieved in the static environment, and up to 85% in the presence of human movements. Finally, adopting the proposed framework we outline a fully developed system, named Marauder, that support user interface interaction and real-time voice-enabled location prediction.

Automated methods for the location of the boundaries of chromatographic peaks

Several simple techniques are presented for the identification of the boundaries of chromatographic peaks. These methods provide a significant reduction in the time needed to perform the rapid, automatic calculation of the central peak moments and to evaluate the quality of a separation while improving the accuracy of the measurements of column efficiencies. It was found that the identification of the peak boundaries as functions of the peak widths and the examination of the slope of the signal to noise versus time plot are viable alternatives to a manual determination.

A hybrid intelligent system and its application to fault detection and diagnosis

This paper proposes a hybrid system that integrates the SOM (Self Organizing Map) neural network, the kMER (kernel-based Maximum Entropy learning Rule) algorithm and the Probabilistic Neural Network (PNN) for data visualization and classification. The rationales of this hybrid SOM-kMER-PNN model are explained, and the applicability of the proposed model is demonstrated using two benchmark data sets and a real-world application to fault detection and diagnosis. The outcomes show that the hybrid system is able to achieve comparable classification rates when compared to those from a number of existing classifiers and, at the same time, to produce meaningful visualization of the data sets.